Right-Time Decision Making 

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The Data Warehousing Institute, 2006

 

8 

Dangers of Not Collecting the Data. Unless the process of merging historical and 

operational data is brain-dead simple, most business users will forego the effort. They

will simply not do the analysis, or make a half-baked decision based on partial

information, or rely totally on intuition to make a decision or projection to the

detriment of the company. The worst scenario is when a few ambitious business users

try to cobble the data together but make errors along the way because they do not

understand the nuances of the SQL query language or semantic discrepancies within

the data itself. When this happens, the business users and the company risk making

costly decisions with inaccurate data.

2. Re-architecting a Data Warehouse Is Costly

Data Warehouses to the Rescue? The traditional way to integrate information stored 

in multiple systems for reporting and analysis purposes is to build a data warehouse.

Unfortunately, most data warehouses extract historical information in large batch

jobs, usually over night or on the weekends. Retrofitting a data warehouse to support

right-time data feeds can be very complex and expensive it s almost comparable to

changing the engine of an airplane in flight!

However, when the value of the right-time information is high enough, executives

will step in to fund the development of a new application that requires the creation of

a right-time data infrastructure. For instance, Basel II and other regulations are

forcing many large financial services firms to do a better job managing financial

exposure and risk across multiple product lines and divisions. But rather than retrofit

an existing data warehouse or data integration infrastructure, most are building new

risk management applications supported by new right-time data architectures. In other

words, organizations are leaving data warehouses intact to provide historic context for

decision making, but developing new right-time architectures to support just-in-time

analysis.

Flexibility Required. Another challenge is that the sources of right-time information 

are not constant. The right-time information that workers need today will differ from

the right-time information they will want to examine tomorrow. Continually changing

business requirements will require IT departments to source right-time information

from different internal applications as well as external ones, such as Web Services,

Web sites, and syndicated data sources (e.g. Nielsen data.) Organizations that have

developed rigid architectures for delivering right-time information will be forced to

continually rewrite the interfaces between systems at great expense.

3. Limitations of Current BI Products

Too Many BI Products. When the data infrastructure is too costly to build or retrofit 

to support right-time information, many companies look to their analytic applications

and BI products to pick up the slack. One problem with BI products is that there are

too many of them! Research from TDWI shows that companies have an average of

Merging

historical and

operational

data must be

brain-dead

sim  le.

Retrofitting a

DW to support

right-time data

is like

changing the

engine of an

airplane in

fli  ht!